Hands-On Exercise 3

Author

Yang Jun

In this Hands-On Exercise, we explore how to add interactive elements and animations to visualisations using appropriate R packages.

Interactive Data Viz

Getting Started

Install and Load Required Packages

The code chunk below will be used to check if these packages have been installed and also will load them onto your working R environment:

pacman::p_load(ggiraph, plotly, patchwork, DT, tidyverse)

Import the Data

exam_data <- read_csv("data/Exam_data.csv")

Rows: 322 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): ID, CLASS, GENDER, RACE
dbl (3): ENGLISH, MATHS, SCIENCE

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

ggigraph Methods

Tooltips

The following code generates an interactive version of a dotplot, and displays the ID value (student ID) when the cursor hovers over a given dot.

p <- ggplot(data=exam_data,
            aes(x=MATHS)) +
  geom_dotplot_interactive(
    aes(tooltip=ID),
    stackgroups = TRUE,
    binwidth = 1,
    method = 'histodot'
  ) +
  scale_y_continuous(NULL, breaks = NULL)

girafe(
  ggobj = p,
  width_svg = 6,
  height_svg = 6*0.618
)

The following code shows how the tooltip content can be customised:

exam_data$tooltip <- c(paste0(
  "Name = ", exam_data$ID,
  "\n",
  "Class = ", exam_data$CLASS
))

p <- ggplot(data=exam_data,
            aes(x=MATHS)) +
  geom_dotplot_interactive(
    aes(tooltip=tooltip),
    stackgroups = TRUE,
    binwidth = 1,
    method = 'histodot'
  ) +
  scale_y_continuous(NULL, breaks = NULL)

girafe(
  ggobj = p,
  width_svg = 8,
  height_svg = 8*0.618
)

Tooltips can be formatted using CSS, like so:

tooltip_css <- "background-color:white; font-style:bold; color: black"

p <- ggplot(data=exam_data,
            aes(x=MATHS)) +
  geom_dotplot_interactive(
    aes(tooltip=ID),
    stackgroups = TRUE,
    binwidth = 1,
    method = 'histodot'
  ) +
  scale_y_continuous(NULL, breaks = NULL)

girafe(
  ggobj = p,
  width_svg = 6,
  height_svg = 6*0.618,
  options = list(opts_tooltip(css = tooltip_css))
)

Tooltips can even be customised using a function, for example to show computed statistics:

tooltip <- function(y, ymax, accuracy=0.01) {
  mean <- scales::number(y, accuracy = accuracy)
  sem <- scales::number(ymax - y, accuracy = accuracy)
  paste("Mean maths scores:", mean, "+/-", sem)
}

gg_point <- ggplot(data=exam_data,
                   aes(x=RACE)) +
  stat_summary(aes(y=MATHS,
                   tooltip = after_stat(tooltip(y,ymax))),
               fun.data = 'mean_se',
               geom = GeomInteractiveCol,
               fill = 'light blue'
               ) +
  stat_summary(aes(y=MATHS),
               fun.data = mean_se,
               geom = 'errorbar', width = 0.2, size = 0.2
    
  )

girafe(ggobj = gg_point,
       width_svg = 8,
       height_svg = 8*0.618)

Hover effect (using data_id)

Here, when we hover over a data point, all other points with the same CLASS value (i.e. classmates) will be highlighted as well.

p <- ggplot(data=exam_data,
            aes(x=MATHS)) +
  geom_dotplot_interactive(
    aes(data_id = CLASS),
    stackgroups = TRUE,
    binwidth = 1,
    method = 'histodot') +
  scale_y_continuous(NULL, breaks = NULL)

girafe(
  ggobj = p,
  width_svg = 6,
  height_svg = 6*0.618
)

By default, the highlighted dots are shown in orange. This can be changed using CSS:

p <- ggplot(data=exam_data,
            aes(x=MATHS)) +
  geom_dotplot_interactive(
    aes(data_id = CLASS),
    stackgroups = TRUE,
    binwidth = 1,
    method = 'histodot') +
  scale_y_continuous(NULL, breaks = NULL)

girafe(
  ggobj = p,
  width_svg = 6,
  height_svg = 6*0.618,
  options = list(
    opts_hover(css = "fill:#202020;"),
    opts_hover_inv(css = "opacity:0.2;")
  )
)

Combining tooltips and hover effect

Tooltips and hover effect can be combined, so that when we mouse over a data point, all the data points of the same class will be highlighted, and at the same time the name of the class will also be shown:

p <- ggplot(data=exam_data,
            aes(x=MATHS)) +
  geom_dotplot_interactive(
    aes(tooltip = CLASS,
        data_id = CLASS),
    stackgroups = TRUE,
    binwidth = 1,
    method = 'histodot'
  ) +
  scale_y_continuous(NULL, breaks = NULL)

girafe(
  ggobj = p,
  width_svg = 6,
  height_svg = 6*0.618,
  options = list(
    opts_hover(css = "fill:#202020;"),
    opts_hover_inv(css = "opacity:0.2;")
  )
)

Coordinated multiple views

By combining ggiraph and patchwork, we can create coordinated graphs such that when a data point in the first graph is selected, the corresponding data point in the second graph will also be highlighted.

p1 <- ggplot(data=exam_data,
             aes(x=MATHS)) +
  geom_dotplot_interactive(
    aes(data_id = ID),
    stackgroups = TRUE,
    binwidth = 1,
    method = 'histodot'
  ) +
  coord_cartesian(xlim=c(0,100)) +
  scale_y_continuous(NULL, breaks = NULL)

p2 <- ggplot(data=exam_data,
             aes(x=ENGLISH)) +
  geom_dotplot_interactive(
    aes(data_id = ID),
    stackgroups = TRUE,
    binwidth = 1,
    method = 'histodot'
  ) +
  coord_cartesian(xlim=c(0,100)) +
  scale_y_continuous(NULL, breaks = NULL)

girafe(code = print(p1 + p2),
       width_svg = 6,
       height_svg = 3,
       options = list(
         opts_hover(css = "fill:#202020;"),
         opts_hover_inv(css = "opacity:0.2;")
       )
)

Interactive Web Data Viz using plotly

Simple scatter plot using `plot_ly()` method

plot_ly(data = exam_data,
        x = ~MATHS,
        y = ~ENGLISH,
        color = ~CLASS)

No trace type specified:
  Based on info supplied, a 'scatter' trace seems appropriate.
  Read more about this trace type -> https://plotly.com/r/reference/#scatter

No scatter mode specifed:
  Setting the mode to markers
  Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode

Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors

Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors

Same scatter plot using plotly: `ggplotly()` method

p <- ggplot(data=exam_data,
            aes(x=MATHS,
                y=ENGLISH)) +
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))

ggplotly(p)

Coordinated multiple views using `ggplotly()`

d <- highlight_key(exam_data)

p1 <- ggplot(data=d,
            aes(x=MATHS,
                y=ENGLISH)) +
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))

p2 <- ggplot(data=d,
            aes(x=MATHS,
                y=SCIENCE)) +
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))

subplot(ggplotly(p1),
        ggplotly(p2))

Interactive Data Tables

DT::datatable(exam_data, class= "compact")

Linking data table with plot

d <- highlight_key(exam_data)
p <- ggplot(d,
            aes(ENGLISH, MATHS)) +
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))

gg <- highlight(ggplotly(p), 'plotly_selected')

crosstalk::bscols(gg,
                  DT::datatable(d),
                  widths=5)

Setting the `off` event (i.e., 'plotly_deselect') to match the `on` event (i.e., 'plotly_selected'). You can change this default via the `highlight()` function.

Animation

Getting Started

Install and Load Required Packages

pacman::p_load(readxl, gifski, gapminder,
               plotly, gganimate, tidyverse)

Installing package into 'C:/Users/yang_/AppData/Local/R/win-library/4.2'
(as 'lib' is unspecified)

Warning: unable to access index for repository http://www.stats.ox.ac.uk/pub/RWin/bin/windows/contrib/4.2:
  cannot open URL 'http://www.stats.ox.ac.uk/pub/RWin/bin/windows/contrib/4.2/PACKAGES'

package 'gifski' successfully unpacked and MD5 sums checked

The downloaded binary packages are in
    C:\Users\yang_\AppData\Local\Temp\Rtmpy6Q5gL\downloaded_packages


gifski installed

Warning: package 'gifski' was built under R version 4.2.3

Installing package into 'C:/Users/yang_/AppData/Local/R/win-library/4.2'
(as 'lib' is unspecified)

Warning: unable to access index for repository http://www.stats.ox.ac.uk/pub/RWin/bin/windows/contrib/4.2:
  cannot open URL 'http://www.stats.ox.ac.uk/pub/RWin/bin/windows/contrib/4.2/PACKAGES'

package 'gapminder' successfully unpacked and MD5 sums checked

The downloaded binary packages are in
    C:\Users\yang_\AppData\Local\Temp\Rtmpy6Q5gL\downloaded_packages


gapminder installed

Warning: package 'gapminder' was built under R version 4.2.3

Import the Data

cols <- c('Country', 'Continent')
globalPop <- read_xls('data/GlobalPopulation.xls',
                      sheet='Data') %>%
  mutate(across(all_of(cols), ~ factor(.x))) %>%
  mutate(Year = as.integer(Year))

Adding animations using `gganimate`

Population bubble plot

Start with a static bubble plot:

ggplot(data=globalPop,
       aes(x=Old,
           y=Young,
           size=Population,
           colour=Country)) +
  geom_point(alpha = 0.7,
            show.legend = FALSE) +
  scale_color_manual(values = country_colors) +
  scale_size(range = c(2,12)) +
  labs(title = 'Year: {frame_time}',
       x = '% Aged',
       y = '% Young')

Add animation by creating transitions across Years:

ggplot(data=globalPop,
       aes(x=Old,
           y=Young,
           size=Population,
           colour=Country)) +
  geom_point(alpha = 0.7,
            show.legend = FALSE) +
  scale_color_manual(values = country_colors) +
  scale_size(range = c(2,12)) +
  labs(title = 'Year: {frame_time}',
       x = '% Aged',
       y = '% Young') +
  transition_time(Year) +
  ease_aes('linear')

Adding animations using `plotly`

Using `ggplotly()` method

gg <- ggplot(data=globalPop,
             aes(x=Old,
                 y=Young,
                 size=Population,
                 colour=Country)) +
  geom_point(aes(size = Population,
                 frame = Year),
             alpha = 0.7,
             show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2,12)) +
  labs(x = '% Aged',
       y = '% Young')

Warning: Ignoring unknown aesthetics: frame

ggplotly(gg)

Warning in p$x$data[firstFrame] <- p$x$frames[[1]]$data: number of items to
replace is not a multiple of replacement length

Using `plot_ly()` method

bp <- globalPop %>%
  plot_ly(x = ~Old,
          y = ~Young,
          size = ~Population,
          color = ~Continent,
          frame = ~Year,
          text = ~Country,
          hoverinfo = 'text',
          type = 'scatter',
          mode = 'markers')

bp

Interactive Data Viz

Getting Started

Install and Load Required Packages

Import the Data

ggigraph Methods

Tooltips

Hover effect (using data_id)

Combining tooltips and hover effect

Coordinated multiple views

Interactive Web Data Viz using plotly

Simple scatter plot using plot_ly() method

Same scatter plot using plotly: ggplotly() method

Coordinated multiple views using ggplotly()

Interactive Data Tables

Linking data table with plot

Animation

Getting Started

Install and Load Required Packages

Import the Data

Adding animations using gganimate

Population bubble plot

Adding animations using plotly

Using ggplotly() method

Using plot_ly() method

Simple scatter plot using `plot_ly()` method

Same scatter plot using plotly: `ggplotly()` method

Coordinated multiple views using `ggplotly()`

Adding animations using `gganimate`

Adding animations using `plotly`

Using `ggplotly()` method

Using `plot_ly()` method